Next Article in Journal
Assessing the Safety and Probiotic Characteristics of Lacticaseibacillus rhamnosus X253 via Complete Genome and Phenotype Analysis
Next Article in Special Issue
Current Updates on the Role of Microbiome in Endometriosis: A Narrative Review
Previous Article in Journal
Metal Nanoparticles to Combat Candida albicans Infections: An Update
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Microorganisms
Volume 11
Issue 1
10.3390/microorganisms11010139
microorganisms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Compositional Changes in the Vaginal Bacterial Microbiome of Healthy Pregnant Women across the Three Gestational Trimesters in Ismailia, Egypt

by
Mariam E. Amin
1,
Marwa Azab
1,
Amro Hanora
1,*,
Khaled Atwa
2 and
Sarah Shabayek
1
1
Department of Microbiology and Immunology, Faculty of Pharmacy, Suez Canal University, Ismailia 41522, Egypt
2
Department of Obstetrics and Gynecology, Faculty of Medicine, Suez Canal University, Ismailia 41522, Egypt
*
Author to whom correspondence should be addressed.
Microorganisms 2023, 11(1), 139; https://doi.org/10.3390/microorganisms11010139
Submission received: 28 November 2022 / Revised: 23 December 2022 / Accepted: 3 January 2023 / Published: 5 January 2023
(This article belongs to the Special Issue Vaginal Microbiome in Women's Health)
Browse Figures
Review Reports Versions Notes

Abstract

:
The composition of the vaginal microbiome may lead to adverse pregnancy outcomes. Normal pregnancy is associated with changes in the vaginal bacterial community composition, which tend to be more enriched with one or two Lactobacillus species promoting a healthy vagina and favorable birth outcomes. The aim of the current study was to determine compositional changes in the healthy vaginal microbiome composition during the three trimesters of pregnancy in Ismailia, Egypt using Illumina MiSeq sequencing of the V3–V4 region of the 16S rRNA. The phylum Firmicutes and the genus Lactobacillus dominated across the three trimesters of pregnancy. L. iners was the most abundant species. However, L. coleohominis and L. reuteri represented the least dominant vaginal lactobacilli. Core microbiome analyses showed the Lactobacillus genus and L. iners species to have the highest prevalence in all the samples of our study groups. The phylum Firmicutes was found to be negatively correlated with almost all other vaginal phyla during pregnancy. Likewise, a negative correlation between Lactobacillus and almost all other genera was detected, including significant negative correlations with Dialister and Prevotella. Furthermore, negative correlations of L. iners were detected with almost all other species, including a significant negative correlation with L. helveticus, G. vaginalis, S. anginosus, and S. agalactiae.

1. Introduction

Recently, the number of studies investigating the composition of the vaginal ecosystem and their impact on female reproductive health is increasing [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]. Next-generation sequencing technology provides a detailed profiling of the human vaginal microbiome; however, the exact mechanisms defining its role in women’s reproductive health remains unknown. Vaginal microbiome is crucial for both maternal and neonatal health during pregnancy [17].
The vaginal microbiome of healthy pregnant women tends to be more enriched and dominated with one or two Lactobacillus species compared to the non-pregnant state [18,19]. The hormone levels in pregnancy favor the dominance of Lactobacillus species while Gardnerella vaginalis and other anaerobic bacteria decrease. Pregnancy is associated with high levels of circulating estrogen produced from the ovaries and the placenta [5,20]. The elevated estrogen levels promote glycogen deposition in the vaginal epithelial cells, which, in turn, promotes the proliferation of different Lactobacillus species. Due to these changes, the composition of the vaginal microbiome is stable with limited variations during pregnancy [5,17,21,22]. Such dominance of Lactobacillus species has been linked to the low production of proinflammatory cytokine, healthy vagina, and favorable birth outcomes [5,23,24].
Changes in the vaginal microbiome composition during pregnancy commonly include transitions between different Lactobacillus species [3,18]. Five community state types (CSTs) of bacteria have been described in asymptomatic women at a reproductive age between 12 and 45 years [5,15,16]. These are CST I, which is dominated by L. crispatus; CST II, which is dominated by L. gasseri; CST III, which is dominated by L. iners; and CST V, which is dominated by L. jensenii, while CST IV is depleted in Lactobacillus species and enriched with diverse anaerobic bacteria, such as Atopobium vaginae, G. vaginalis, Prevotella, Megasphaera, Dialister, Sneathia, Peptoniphilus, Mobiluncus, and Finegoldia. These are usually reported in association with bacterial vaginosis (BV) and increased risk of preterm birth [25,26].
The composition of the vaginal microbiome may also be shaped through ethnic, genetic, cultural, and behavioral differences between hosts [10,14,15,27]. Vaginal microbiomes dominated by L. iners or with more diverse anaerobic communities, mostly G. vaginalis and Candidatus Lachnocurva vaginae, which is formerly known as bacterial vaginosis-associated bacterium 1 (BVAB1), have been reported for most sub-Saharan African pregnant women [5,15,27]. However, studies describing vaginal microbiomes in Egypt or nearby regions are still sparse. The aim of the present study was to investigate the vaginal microbiome composition of healthy pregnant women in Egypt during the first, second, and third trimesters compared to healthy non-pregnant women. The potential correlations between the vaginal microbiota during pregnancy versus non-pregnancy state have also been demonstrated. This has been achieved through next-generation sequencing of the V3–V4 hypervariable region of the 16S rRNA gene.

2. Materials and Methods

2.1. Ethical Statement

This study was approved by the scientific research ethics committee of the Faculty of Pharmacy, Suez Canal University, Ismailia, Egypt (Reference number 201803PHDH1). The study was conducted in accordance with the principles of Helsinki Declaration. Informed consent was obtained from all women prior to enrollment.

2.2. Participant Enrollment

We analyzed samples from 36 pregnant women and 12 non-pregnant women attending the obstetrics and gynecology clinics in Suez Canal University Educational Hospital and El Sheikh Zayed Governmental Health Office from July 2020 to October 2020. All women were healthy with no known medical issues. Participants were sampled cross sectionally and were divided into four study groups: (1) healthy non-pregnant women (control group) (n = 12), (2) healthy pregnant women in the first trimester of pregnancy (n = 12), (3) healthy pregnant women in the second trimester of pregnancy (n = 12), (4) healthy pregnant women in the third trimester of pregnancy (n = 12). Inclusion and exclusion criteria were employed, as has been previously described [10,28,29,30]. All participants were aged between 18–40 years (Supplementary Table S1) with regular menstruation and had no sexual intercourse, tampon usage, douching, or vaginal creams in the preceding 72 h. All non-pregnant participants had suffered no bacterial vaginosis and had a vaginal pH ≤ 4.5. All pregnant women showed normal singleton gestation with no maternal comorbidities, such as gestational diabetes type A2, diabetes mellites, hypertension, or any other chronic diseases. Participants were excluded for the following reasons: smoking, alcohol intake, bacterial vaginosis, principle dietary changes, history of cancer, immunocompromised status, endocrine disturbances, medication usage within the last 6 months, e.g., antibiotics, hormones, corticosteroids, cytokines, probiotics. The participants were divided according to pregnancy status into four groups: non-pregnant, pregnant during the first trimester, pregnant during the second trimester, and pregnant during the third trimester. Obstetrical dating for gestational age was determined, as indicated by the American College for Obstetricians and Gynecologists (ACOG) (https://www.acog.org/, accessed on 23 December 2022). First trimester is from first day of last menstrual period (LMP) to 13 weeks and 6 days. Second trimester is from 14 weeks and 0 days to 27 weeks and 6 days. Third trimester is from 28 weeks and 0 days to 40 weeks and 6 days.

2.3. Sample Collection

Vaginal samples were obtained using sterile cotton swabs inserted about 4 to 5 cm into the vagina, twisted and wiped in full circles in order to collect vaginal material on all sides of the tip, and kept in the vagina for 20 s [31,32,33]. Afterwards, the swab was carefully removed and placed in a sterile Eppendorf tube containing 1 mL of sterile phosphate-buffered saline (PBS) (Sigma, Darmstadt, Germany, Cat. No. 806552, pH 7.4) with vigorous shaking [29,34]. Samples were transported on ice and immediately stored at −80 °C [9,29,34,35,36].

2.4. The pH Measurement

Vaginal pH of non-pregnant women (control group) was measured using commercial pH strips in the pH range 4.5–10.0 (Sigma, Germany, Cat. No. P4536). The color of the test pH indicator was compared to a standard color chart provided with the pH applicator. Non-pregnant women with pH exceeding 4.5 were excluded from the study [21,28,37]. As recommended by Agaard et al., 2012 [28], vaginal pH was not recorded for pregnant participants; however, they were excluded if presenting any signs and symptoms for bacterial vaginosis.

2.5. DNA Extraction

Samples were released from −80 °C conditions and allowed to reach room temperature. Microbial DNA was extracted using Dneasy PowerSoil DNA extraction kit (Qiagen, Valencia, CA, USA, Cat. No. 12888-50) [38,39], according to manufacturer’s instructions. Extraction steps were conducted after gentle vortex of the collection tubes containing swab tips to induce better dispersion of the trapped bacteria.

2.6. PCR Amplification of 16S rRNA Gene and MiSeq Illumina Sequencing

PCR targeting amplification of the hypervariable regions V3–V4 of 16S rRNA gene was performed according to the Illumina 16S rDNA Metagenomic Sequencing Library Preparation protocol (Illumina, Inc., San Diego, CA, USA) [40] using the following primer sequences, appended to Illumina adaptor (underlined):
Forward primer: 5’-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG 3’.
Reverse primer: 5’GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3’.
PCR reactions were performed in volumes of 100 µL using OnePCRTM Ultra Master Mix (GeneDirex®, Taichung City, Taiwan, Cat. No. MB208-0100). PCR was performed in a thermal cycler (BIORAD T100 Thermal Cycler) with the following conditions: Initial denaturation at 95 °C for 3 min, followed by 25 cycles of denaturation at 95 °C for 30 s, annealing at 55 °C for 30 s, and extension at 72 °C for 30 s. Then, final extension at 72 °C for 5 min. Size and quality of PCR amplification products was verified with agarose gel electrophoresis. The primer pair sequences used for the V3–V4 region were employed to create a single amplicon of approximately 550 bp. Amplicons from negative controls were analyzed to detect false-positive reactions due to contaminants in swabs, extraction reagents, and PCR mixture. Library preparation and high-throughput Illumina MiSeq paired-end 2 × 300 base sequencing was performed at IGA Technology Services (Udine, Italy). Negative controls, including an empty swab for DNA extraction and amplified nuclease-free water without extracted DNA, were also sequenced and analyzed along with the samples.

2.7. Bioinformatic Analysis

Raw sequences were imported into Quantitative Insights into Microbial Ecology 2 (QIIME2 version 2021.4) [33] as Casava 1.8 paired-end demultiplexed fastq format. Analysis began with generating interactive positional quality plots (Supplementary Figure S1 panels A and B) by randomly subsampling 10,000 out of 5,930,186 sequences without replacement. This was done using the QIIME2 command “qiime demux summarize”. Positional quality plots were then used to decide the best positions for trimming and truncation. Then, we used DADA2 [41] through qiime dada2 plugin for filtering out noisy sequences, correcting errors in marginal sequences, removing chimeric sequences, removing singletons, joining denoised paired-end reads, and then dereplicating these sequences producing unique amplicon sequence variants (ASVs) feature table. The following parameters were used for DADA2 pipeline: forward read sequences were truncated at 300, while reverse read sequences were truncated at 260. The first 24 bases at 5′ end of each sequence were trimmed for both forward and reverse sequences [42]. Taxonomy assignment of ASVs was performed based on trained RDP’s naive Bayesian classifier at 99% sequence similarity against the Greengenes [43] database (https://data.QIIME2.org/2021.4/common/gg-13-8-99-nb-classifier.qza, accessed on 11 August 2022). Further taxonomic classification of unrecognized features in our data set was performed using NCBI-BLAST links, which were complementary to QIIME2 and provided with dada2-rep-seq.qzv visualization file produced from DADA2 pipeline. Sequences were rarefied with QIIME2 script “qiime diversity alpha-rarefaction’’ command. Alpha diversity (within sample variation) and beta diversity (between sample variation) analysis were performed through running the QIIME2 scripts “qiime diversity core-metrics-phylogenetic” command. Statistical significance and visualization of violin plots of the alpha diversity indices (Observed, Chao1, Shannon, and Simpson) and Pielou’s evenness was performed using “ggstatsplot” R package, using the function “ggbetweenstats”; the p value was measured by pairwise Kruskal–Wallis test and adjusted using Holm adjustment. Dunn pairwise test was also performed. Beta diversity distances, including Bray–Curtis, Jaccard, and unweighted and weighted UniFrac distances, were represented via generating principal coordinates analysis (PCoA) plots at feature level through MicrobiomeAnalyst [44,45] using analysis of group similarities (ANOSIM) statistical method. Pairwise permutational multivariate analysis of variance (PERMANOVA) test in QIIME2 using pseudo-F statistic was also done. This was applied to measuring the amount of variation in beta diversity between groups. A p value < 0.05 is considered statistically significant. Taxonomy bar plots, core microbiome, and discriminative taxa between study groups were analyzed using MicrobiomeAnalyst (https://www.microbiomeanalyst.ca/, accessed on 15 December 2022) [44,45] and “ggplot2” R package of R (v 4.2.0). Discriminative taxa between study groups were measured by linear discriminant analysis (LDA) effective size (LEfSe). LDA score ≥ 2 and a p value < 0.05 was considered statistically significant. Correlations between taxa across pregnancy trimesters and non-pregnancy state were analyzed by Spearman correlation analysis using the “rcorr” function of R package “Hamsic” (r ≥ ±0.6, p ≤ 0.05). The “ggpairs” function of the R package “GGally” was used for plotting correlation between pairs of the vaginal microbiota during each trimester of pregnancy at phylum, genus, and species levels.

2.8. Community State Types (CST) Assignment

Taxonomic profiles of our vaginal bacterial communities were classified into CSTs using VALENCIA (VAginaL community state typE Nearest CentroId classifier) [46]. This is a nearest centroid-based tool, which sorts samples according to their similarity to a set of reference centroids.

2.9. Data Availability

The raw sequences were submitted in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA), under the BioProject ID PRJNA877217 https://www.ncbi.nlm.nih.gov/sra/PRJNA877217 (accessed on 15 September 2022) involving accession numbers from SAMN30696179 to SAMN30696228.

3. Results

3.1. Sequencing Data Profiles

A total of 4,051,265 read counts were maintained after the quality trimming of Illumina MiSeq raw sequencing data of 48 vaginal samples. The average read counts per sample was 84,401, while minimum and maximum counts per sample were 36,027 and 149,731, respectively. The total number of ASVs features that remained was 927. The plateau was reached at 20,000 within the rarefaction curves (Supplementary Figure S2), confirming the appropriate sampling depth used. The p-max-depth was 41,064. Stacked bar charts showing taxa relative abundance for individual samples are shown in Supplementary Figure S3.

3.2. Taxonomical Classification, Core Microbiome and Bacterial Biomarkers

3.2.1. Taxonomical Classification

Firmicutes, Actinobacteria, and Tenericutes were the most abundant phyla in all study groups (Figure 1A, Supplementary Table S2). The relative abundance of Firmicutes was the highest in the first trimester (97.7%), compared to the second trimester (96.2%), and non-pregnant control group (92.4%), while it was the least abundant in the third trimester (86.3%). However, the relative abundance of Actinobacteria and Tenericutes were the highest in the third trimester (6.4%, 4.4%, respectively) compared to the second trimester (1.4%, 0.9%, respectively) and non-pregnant control group (5.2%, 0.9%, respectively), while they were relatively the least abundant in the first trimester (1.2%, 0.4%, respectively).
The most abundant genera (Figure 1B, Supplementary Table S3) were Lactobacillus, Gardnerella, Ureaplasma, and Streptococcus. The relative abundance of Lactobacillus was the highest in the first trimester (95.4%) compared to the second trimester (94.7%) and non-pregnant control group (89.2%), while it was the least abundant in the in third trimester (81.8%). However, the relative abundance of Gardnerella and Ureaplasma was the highest in the third trimester (5.9%, 4.4%, respectively) when compared to the second trimester (1.3%, 0.9%, respectively) and non-pregnant control group (2.6%, 0.9%, respectively), while their relative abundance was the least in the first trimester (0.9%, 0.4%, respectively). The relative abundance of the genus Streptococcus was the highest in the third trimester (2.1%) when compared to the first trimester (0.5%) and non-pregnant control group (0.6%), while it was the least in the second trimester (0.05%).
On the species level (Figure 1C, Supplementary Table S4), L. iners was the most abundant species in all study groups. However, it was relatively the most abundant species in the second trimester (65.3%) and non-pregnant control group (66.6%) when compared to the first trimester (52.2%), while it was relatively the least abundant in the third trimester (33%). Other detected Lactobacillus species included L. helveticus, L. jensenii, L. gasseri, L. coleohominis, and L. reuteri. The relative abundance of L. helveticus was the highest in the third trimester (25.4%) when compared to the second trimester (18.8%) and first trimester (8.4%), while its relative abundance was the lowest in the non-pregnant control group (2.9%). The relative abundance of L. jensenii was the highest in the first trimester (23.1%) when compared to the third trimester (16.1%) and non-pregnant control group (13.4%), while its relative abundance was the least in the second trimester (9.9%). The relative abundance of L. gasseri was the highest in the first trimester (11.3%) when compared to the third trimester (6.3%) and non-pregnant control group (5.9%), while its abundance was the least in second trimester (0.17%). L. coleohominis and L. reuteri were detected as minor Lactobacillus species. The relative abundance of L. coleohominis ranged from 0.22% in the third trimester compared to 0.19% in the first trimester, while its relative abundance was the least in the second trimester and non-pregnant control group (0.09%, 0.085%, respectively). The relative abundance of L. reuteri was 0.49% the in third trimester, 0.42% in the second trimester, and 0.29% in the non-pregnant control group, while its abundance was the least in the first trimester (0.2%).
Among the non-Lactobacillus species (Figure 1D, Supplementary Table S5), Bifidobacterium breve, Peptostreptococcus anaerobius, Streptococcus anginosus, Ureaplasma parvum, and Streptococcus agalactiae were frequently detected. The relative abundance of B. breve, P. anaerobius, and S. anginosus was the highest in the non-pregnant control group. The relative abundance of B. breve was 2.5% in the non-pregnant control group compared to 0.04% in the first trimester, while it was nearly absent in the second and third trimesters. Similarly, the relative abundance of P. anaerobius was 0.29% in the non-pregnant control group compared to 0.28% in the first trimester, while its relative abundance was the lowest in the second and third trimesters (0.02%, 0.019%, respectively). The relative abundance of S. anginosus was 0.48% in the non-pregnant control group compared to 0.12% in the third trimester, while its relative abundance was as its lowest in the second and first trimesters (0.03%, 0.01%, respectively). However, the relative abundance of U. parvum and S. agalactiae was the highest in the third trimester. The relative abundance of U. parvum was 4.4% in the third trimester compared to 0.9% in the second trimester and the non-pregnant control group, while its relative abundance was the lowest in the first trimester (0.4%). The relative abundance of S. agalactiae was 1.9% in the third trimester compared to 0.5% in the first trimester, while its relative abundance was the lowest in the non-pregnant control group (0.005%) and it was absent in the second trimester.
According to Ravel et al., 2011 [15], CST classifications using VALENCIA [46] are shown in Figure 2 panels A and B, Table 1 and Supplementary Table S6. The L. iners-dominant CST III was most commonly assigned in all study groups, representing more than two-thirds of our samples (32/48, 66%). This was followed by CST V (7/48, 14.58%), CST IV (5/48, 10.4%), and CST II (4/48, 8.3%). CST III represented 91.67% of the samples in the second trimester (11/12) compared to 75% (9/12) in the non-pregnant control group, and half of the samples in both the first trimester (50%, 6/12) and third trimester (50%, 6/12). Assignments to CST III-A (L. iners highly dominant) were observed in 31.25% (15/48) of our samples, including 16.67% (2/12) in the third trimester, 41.67% (5/12) in the second trimester, 33.33% (4/12) in the first trimester compared to 33.33% (4/12) in the non-pregnant control group. Meanwhile, assignments to CST III-B (slightly lower L. iners abundance) were detected in 35.42% (17/48) samples, involving 33.33% (4/12) in the third trimester, 50% (6/12) in the second trimester, and 16.67% (2/12) in the first trimester compared to 41.67% (5/12) in the non-pregnant control group. CST assignments to CST II, L. gasseri-dominant, were observed in 16.67% (2/12) in the third trimester, 8.33% (1/12) in the first trimester and the non-pregnant control group, and it was not recorded in the second trimester. Assignments to CST V, L. jensenii-dominant, were detected in 25% (3/12) in the third trimester, 16.67% (2/12) in the first trimester, and 8.33% (1/12) in the second trimester and the non-pregnant control group. For non-Lactobacillus-dominated CST, assignments to CST IV_C (diverse facultative and strictly anaerobic bacteria dominance) were detected in 16.67% (2/12) of the first trimester, 8.33% (1/12) in the third trimester and non-pregnant control group, and was not found in the second trimester. CST IV-C0 (slightly abundance of Prevotella) was detected in 8.33%(1/12) of the first trimester, CST IV-C1 (Streptococcus dominated) was observed in 8.33% (1/12) of the third trimester, and CST IV-C3 (Bifidobacterium dominated) was detected in 8.33% (1/12) of first trimester and 8.33% (1/12) of the non-pregnant control group, while CST IV_B (high relative abundance of G. vaginalis) was only detected in 8.33% (1/12) in the first trimester.

3.2.2. Bacterial Biomarkers

Bacterial biomarkers of each study group were detected through Linear discriminant analysis effect size (LEfSe) using MicrobiomeAnalyst. At species level, B. breve was detected to be significantly abundant in the non-pregnant control group compared to the pregnant group. However, across pregnancy trimesters, L. gasseri was significantly abundant in first trimester and S. agalactiae in third trimester.

3.3. Alpha Diversity

Alpha diversity, as calculated by the Alpha diversity indices Observed (p = 0.62, Figure 3A), Chao1 (p = 0.62, Figure 3B), Shannon (p = 0.19, Figure 3C), and Simpson (p = 0.18, Figure 3D), revealed non-significant differences in the vaginal community composition within study groups. This was further confirmed by Pielou’s evenness alpha diversity (p = 0.22, Figure 4, Supplementary Table S7). There was a relative homogeneity of the vaginal microbiome both across the three trimesters of pregnancy and, in comparison, to the non-pregnant status.

3.4. Beta Diversity

The dissimilarities between vaginal microbial communities were detected through measuring beta diversity by calculating the Bray–Curtis and Jaccard indices. Significant differences in the composition of the vaginal microbial communities among study groups were revealed by Bray–Curtis (R = 0.059326, p < 0.037, ANOSIM) and Jaccard (R = 0.059326, p < 0.037, ANOSIM) dissimilarity distances (Figure 5 panels A and B). These differences were further confirmed by pairwise PERMANOVA tests. There was a significant difference in the composition of the vaginal microbial communities between all study groups except between the vaginal microbial communities of the second and third trimester of pregnancy as they were revealed to be relatively similar when compared to the first trimester and the non-pregnant state. However, phylogenetic dissimilarity distances (Figure 5 panels C and D), unweighted UniFrac (R = 0.014871, p < 0.3, ANOSIM), and weighted UniFrac tests (R = 0.01908, p < 0.151, ANOSIM) revealed non-significant differences between study groups confirming the low diversity of the vaginal microbiome during pregnancy.

3.5. Correlation of Vaginal Microbiota during Pregnancy

Correlation analysis revealed that Firmicutes was negatively correlated with almost all other phyla (Figure 6A, Supplementary Table S8). Likewise, Lactobacilli were negatively correlated with almost all other genera (Figure 6B, Supplementary Table S9) showing significant negative correlation with the genera Dialister and Prevotella. Similarly, L. iners was negatively correlated with almost all other species (Figure 6C, Supplementary Table S10). There was a significant negative correlation between L. iners and other Lactobacillus species, in particular, L. helveticus. There was also a negative correlation between L. iners and other non-Lactobacillus species, demonstrating significant negative correlations with G. vaginalis, S. agalactiae, P. anaerobius, and S. anginosus versus a significant positive correlation between the non-lactobacillus species pairs G. vaginalis and S. agalactiae, G. vaginalis and P. anaerobius, S. aginosus and P. anaerobius. Further positive correlations were found between Finegoldia magna and the species S. agalactiae, P. anaerobius, and S. aginosus.

4. Discussion

A deep investigation of the vaginal environment can provide new perspectives for future health through understanding the pathophysiology of pregnancy and identifying women at risk for adverse pregnancy outcomes. In the current study, we have characterized the vaginal microbiome during normal pregnancy at the three gestational trimesters compared to non-pregnant status, in Ismailia, Egypt through profiling the vaginal microbiota using 16S rRNA next-generation sequencing. We were able to describe fluctuations in the composition of the vaginal microbiota during the three trimesters of pregnancy compared to non-pregnant state.
In general, irrespective to the period, pregnancy was dominated by members of the phylum Firmicutes. As expected, Lactobacillus-dominant profiles were a characteristic feature of healthy vaginal microbiomes during pregnancy. The relative abundance of the genus Lactobacillus was in a range from 81% to 95%, confirming the low biodiversity of healthy vaginal bacterial communities. This is in agreement with other populations worldwide [6,9,10,12,17,18,19,28,30,47] despite differences in methodologies and definitions. The growth-inhibiting ability of vaginal lactobacilli is well-documented and primarily attributed to their role in lowering the vaginal pH by lactic acid production [3,24,48]. Other reported growth-inhibiting strategies included hydrogen peroxide production and release of antimicrobial substances, such as bacteriocins [3,24,48,49,50].
L. iners was the most abundant species in both healthy pregnant and non-pregnant women in our study. L. iners has been frequently reported as the most Lactobacillus-dominant member of the vaginal microbiome of women with African ancestry. A PRISMA-compliant review conducted by Juliana and co-workers [5] in seven sub-Saharan African countries identified L. iners among the most prevalent species. Similarly, Gautam et al., 2015 [51] displayed that L. iners-dominated vaginal microbiome clusters among the most dominant clusters in African women from four sub-Saharan countries. Another study by Jespers et al., 2015 [52] in African women from three sub-Saharan regions demonstrated the higher relative abundance of L. iners in women with normal Nugent score compared to non-African studies. Likewise, Serrano et al., 2019 [10] showed pregnant women of African ancestry with significantly higher representation of L. iners compared to non-African ancestry.
Another species that was commonly detected as a characteristic feature of African vaginal microbiomes is Ca. Lachnocurva vaginae or BVAB1 [15,27]. This bacterium is consistently correlated with pregnancy complications, such as preterm birth [10,35]. However, Ca. Lachnocurva vaginae was not detected among our vaginal microbial communities even when compared to studies using similar approaches for investigating the vaginal microbiome profiles [53,54].
In consistence with others [8,15,38], our correlation analysis demonstrated that the phylum Firmicutes, the genus Lactobacillus, and the species L. iners were negatively correlated with almost all other vaginal taxa. According to Ng et al., 2021 [8], this emphasizes their well-documented exclusionary behavior in the vaginal ecosystem. Furthermore, we found a relative increase in the abundance of Gardnerella and Ureaplasma in the third trimester compared to the first and second trimesters, but this increase was in accordance with a high Lactobacillus abundance. Gardnerella and Ureaplasma are well-known pathogens associated with adverse pregnancy outcomes [55]. However, recent microbiome reports [7,55] point out that vaginal colonization with Ureaplasma and Gardnerella during a pregnancy with Lactobacillus abundance seems to be protective against pregnancy complications. Park et al., 2022 [55] demonstrated that Ureaplasma and Lactobacillus coexistence with high Lactobacillus abundance leads to term birth. In addition, Park et al., 2022 [55], Liu et al., 2022 [7], and DiGuilio, 2015 [16] reported that preterm birth and premature membrane ruptures are only predictive with low or depleted Lactobacillus abundance in the presence of Ureaplasma and Gardnerella. This suggests that the balanced co-colonization of Ureaplasma and Gardnerella in the vaginal ecosystem under Lactobacillus dominance may promote full-term birth and prevent pregnancy complications.
We also found L. iners was negatively correlated with L. helveticus, which was common among our vaginal samples. This could be attributable to the overlapping ecological functions in the vaginal econiche. On the other hand, we detected multiple positive correlations between bacteria associated with bacterial vaginosis or vaginal dysbiosis. We found positive correlations between the pairs of G. vaginalis and S. agalactiae, G. vaginalis and P. anaerobius, and S. aginosus and P. anaerobius. Further positive correlations were found between F. magna and S. aginosus, as well as S. agalactiae and P. anaerobius. This is in line with previous reports which highlighted the importance of the synergistic interplay between non-lactobacillus vaginal taxa leading to Lactobacillus displacement, overgrowth of anaerobic bacteria, rise in vaginal pH, and ultimately vaginal dysbiosis or bacterial vaginosis [56,57,58,59,60,61].
Microbiota profiling through 16S rRNA gene next-generation sequencing is primarily dependent on selecting suitable primer pairs and hypervariable regions of the bacterial 16S rRNA gene [62]. Primer selection may have significant influence on obtaining different results [62]. In the current study, we used universal primers, as per instructions of the standard Illumina protocol [40] spanning the V3–V4 hypervariable region, which is widely used for investigating the human vaginal microbiome [1,2,4,7,12,13,38]. This region has been reported to provide intense discrimination between most microbiota of the urogenital tract [7,13]. However, alternative hypervariable regions of the 16S rRNA were also utilized, such as the V3–V5 [16,28,30] and the V1–V2 or V1–V3 hypervariable regions [62,63]. Prince et al., 2015 [63], indicated primers spanning the V1–V3 region are more suitable for detecting Lactobacillus, Prevotella, and Clostridium species, whereas primers spanning the V3–V5 region are better when considering Enterobacteriaceae and Bifidobacteriaceae. According to Graspeuntner et al., 2018 [62], a greater number of taxa in the vaginal microbiota are detectable under the V3–V4 region compared to V1–V2, which is inappropriate for identifying G. vaginalis. The authors indicated that when considering well-defined vaginal microbiota with proven influence in both health and disease, such as G. vaginalis, Bifidobacterium bifidum, and Chlamydia trachomatis, the V3–V4 region is recommended for 16S rRNA microbiome studies. These taxa are underestimated in V1–V2 16S rRNA vaginal community-based profiles, resulting in failure to correctly assess the bacterial diversity and overestimation of the abundance of other taxa.
The selection of tools and databases for appropriate taxonomic assignation is another important methodological issue that should be considered in 16S rRNA microbiome studies. Similar to others [1,8,38], we used QIIME 2 software and Greengenes database. QIIME 2 has been demonstrated to facilitate the reproducible and comprehensive analysis of diverse microbiome data [64]. Greengenes is a popular taxonomic database, which is widely used in profiling the human vaginal microbiome [65,66,67,68,69,70]. Nonetheless, microbiota profiling by 16S rRNA next-generation sequencing is the preferred approach when compared to traditional culture. Currently, it is the quickest and most accurate means for investigating the human vaginal microbiota.
The present study has several limitations that should be avoided in the future. The small sample size of enrolled participants may not give an accurate demonstration of the composition and diversity of the vaginal microbiomes. In addition, we were unable to collect detailed clinical data that would have been valuable in determining potential correlations based on pregnancy state and associated vaginal community composition. Moreover, 16S rRNA next-generation sequencing was used for profiling vaginal bacterial communities as it allows the simultaneous detection of many taxa. However, this technique has limited taxonomic power, particularly on the species level and is not precise enough when considering taxa with a minor abundance [71,72,73]. Some authors may use species-specific quantitative real-time PCR assays to confirm the identity and relative abundance [74]. However, this approach is hampered by the limited multiplex capability of real-time PCR [75,76]. Alternative approaches, such as whole genome shotgun metagenomic sequencing (WGS), should be considered in future studies. WGS is more precise in revealing bacterial species, diversity, and abundance with added the advantage of providing direct information on functional genes [71,72,73].

5. Conclusions

In conclusion, we investigated the vaginal microbiome composition of healthy pregnant women in Ismailia, Egypt during the first, second, and third trimesters compared to healthy non-pregnant women. As expected, phylum Firmicutes and genus Lactobacillus dominated during the three trimesters of pregnancy. L. iners-dominant vaginal communities CST-III prevailed as L. iners was the most abundant species in both healthy pregnant and non-pregnant women in our study. L. iners was almost negatively correlated with all other species in the vagina, in particular, with G. vaginalis, S. anginosus, and S. agalactiae. In contrast, we found significant positive correlations between the species pairs G. vaginalis and S. agalactiae, P. anaerobius and F. magna, S. aginosus and F. magna, S. agalactiae and P. anaerobius. This highlights the protective role of lactobacilli in promoting vaginal health and emphasizing the potential synergistic interplay among vaginal microbiota to induce vaginal dysbiosis. However, a major limitation of the current study is the small sample size and minimal demographic data. Further studies recruiting larger cohorts with detailed demographics of participants should be considered.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms11010139/s1, Figure S1: Positional quality plots generated in QIIME2 by random subsampling 10,000 out of 5,930,186 sequences without replacement. (A) Positional quality plot for forward reads. (B) Positional quality plots for reverse reads. Figure S2: Alpha rarefaction curves of vaginal microbial communities in our study groups. The lines in this rarefaction curve indicate a sampling depth nearly about 20,000. Therefore, additional sequences above that sampling depth would not lead to the observation of new additional features. Good’s coverage was 100 suggesting the full identification of the majority of vaginal bacterial communities in our samples. Figure S3: Relative taxa abundance in vaginal bacterial communities of Egyptian pregnant women across the three trimesters of pregnancy compared to non-pregnant samples. (A) Stacked bar charts representing relative abundance of genus. (B) Stacked bar charts representing relative abundance of species. Each bar represents one sample. Negative-control represents samples with empty swab for DNA extraction and amplified nuclease-free water without extracted DNA. Figure S4: Relative abundance of non-Lactobacillus species across the three trimesters of pregnancy in comparison to non-pregnant state. Negative-control represents samples with empty swab for DNA extraction and amplified nuclease-free water without extracted DNA. Table S1: Age of participants; Table S2: Relative abundance of taxa at phylum level. Table S3: Relative abundance of taxa at genus level. Table S4: Relative abundance of Lactobacillus species. Table S5: Relative abundance of non-Lactobacillus. Table S6: VALENCIA CST classification output revealed similarity of vaginal microbiota profile of individual sample to each of 13 reference centroids producing 13 similarity scores ranging from 0.0 to 1.0 for each sample. Table S7: Alpha diversity using Pielou’s evenness by measuring Kruskal-Wallis test (pairwise) revealing non-significant difference. Table S8: Spearman correlation between the relative abundances of vaginal microbial communities at phylum level and their p values. Table S9: Spearman correlation between the relative abundances of vaginal microbial communities at genera level and their p values. Table S10: Spearman correlation between the relative abundances of vaginal microbial communities at species level and their p values. Software code S1: R scripts used for downstream analysis.

Author Contributions

Conceptualization, A.H., M.A., and S.S.; Data curation, M.E.A.; Formal analysis, M.E.A.; Investigation, M.E.A., S.S., and K.A.; Methodology, M.A. and S.S.; Project administration, A.H., M.A., and S.S.; Resources, A.H., M.A., S.S., and M.E.A.; Software, M.E.A.; Supervision, A.H.; Validation, M.E.A. and S.S.; Visualization, M.E.A. and S.S.; Writing—original draft, M.E.A. and S.S.; Writing—review and editing, S.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data presented in this study are openly available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA), under the BioProject ID PRJNA877217 https://www.ncbi.nlm.nih.gov/sra/PRJNA877217 (accessed on 15 September 2022) involving accession numbers from SAMN30696179 to SAMN30696228.

Acknowledgments

We thank all gynecologists and nursery staff of El Sheikh Zayed Governmental Health Office, Ismailia, Egypt, due to their help in the collection of the samples during routine examination.

Conflicts of Interest

The authors declare no conflict of interest.

Ethical Statement

This study was approved by the scientific research ethics committee of the Faculty of Pharmacy, Suez Canal University, Ismailia, Egypt (Reference number 201803PHDH1). The study was conducted in accordance with the principles of Helsinki Declaration. Informed consent was obtained from all women prior to enrollment.

References

  1. Bernabeu, A.; Lledo, B.; Díaz, M.C.; Lozano, F.M.; Ruiz, V.; Fuentes, A.; Lopez-Pineda, A.; Moliner, B.; Castillo, J.C.; Ortiz, J.A.; et al. Effect of the vaginal microbiome on the pregnancy rate in women receiving assisted reproductive treatment. J. Assist. Reprod. Genet. 2019, 36, 2111–2119. [Google Scholar] [CrossRef] [PubMed]
  2. Goodfellow, L.; Verwijs, M.C.; Care, A.; Sharp, A.; Ivandic, J.; Poljak, B.; Roberts, D.; Bronowski, C.; Gill, A.C.; Darby, A.C.; et al. Vaginal bacterial load in the second trimester is associated with early preterm birth recurrence: A nested case-control study. BJOG Int. J. Obstet. Gynaecol. 2021, 128, 2061–2072. [Google Scholar] [CrossRef] [PubMed]
  3. Grewal, K.; MacIntyre, D.A.; Bennett, P.R. The reproductive tract microbiota in pregnancy. Biosci. Rep. 2021, 41, BSR20203908. [Google Scholar] [CrossRef] [PubMed]
  4. Hočevar, K.; Maver, A.; Vidmar Šimic, M.; Hodžić, A.; Haslberger, A.; Premru Seršen, T.; Peterlin, B. Vaginal Microbiome Signature Is Associated with Spontaneous Preterm Delivery. Front. Med. 2019, 6, 201. [Google Scholar] [CrossRef]
  5. Juliana, N.C.A.; Peters, R.P.H.; Al-Nasiry, S.; Budding, A.E.; Morré, S.A.; Ambrosino, E. Composition of the vaginal microbiota during pregnancy in women living in sub-Saharan Africa: A PRISMA-compliant review. BMC Pregnancy Childbirth 2021, 21, 596. [Google Scholar] [CrossRef] [PubMed]
  6. Li, D.; Chi, X.Z.; Zhang, L.; Chen, R.; Cao, J.R.; Sun, X.Y.; Yang, H.Q.; Liao, Q.P. Vaginal microbiome analysis of healthy women during different periods of gestation. Biosci. Rep. 2020, 40, BSR20201766. [Google Scholar] [CrossRef]
  7. Liu, L.; Chen, J.; Chen, Y.; Jiang, S.; Xu, H.; Zhan, H.; Ren, Y.; Xu, D.; Xu, Z.; Chen, D. Characterization of Vaginal Microbiota in Third Trimester Premature Rupture of Membranes Patients through 16S rDNA Sequencing. Pathogens 2022, 11, 847. [Google Scholar] [CrossRef]
  8. Ng, S.; Chen, M.; Kundu, S.; Wang, X.; Zhou, Z.; Zheng, Z.; Qing, W.; Sheng, H.; Wang, Y.; He, Y.; et al. Large-scale characterisation of the pregnancy vaginal microbiome and sialidase activity in a low-risk Chinese population. NPJ Biofilms Microbiomes 2021, 7, 89. [Google Scholar] [CrossRef]
  9. Nunn, K.L.; Witkin, S.S.; Schneider, G.M.; Boester, A.; Nasioudis, D.; Minis, E.; Gliniewicz, K.; Forney, L.J. Changes in the Vaginal Microbiome during the Pregnancy to Postpartum Transition. Reprod. Sci. 2021, 28, 1996–2005. [Google Scholar] [CrossRef]
  10. Serrano, M.G.; Parikh, H.I.; Brooks, J.P.; Edwards, D.J.; Arodz, T.J.; Edupuganti, L.; Huang, B.; Girerd, P.H.; Bokhari, Y.A.; Bradley, S.P.; et al. Racioethnic diversity in the dynamics of the vaginal microbiome during pregnancy. Nat. Med. 2019, 25, 1001–1011. [Google Scholar] [CrossRef]
  11. Severgnini, M.; Morselli, S.; Camboni, T.; Ceccarani, C.; Laghi, L.; Zagonari, S.; Patuelli, G.; Pedna, M.F.; Sambri, V.; Foschi, C.; et al. A Deep Look at the Vaginal Environment During Pregnancy and Puerperium. Front. Cell. Infect. Microbiol. 2022, 12, 838405. [Google Scholar] [CrossRef]
  12. Sroka-Oleksiak, A.; Gosiewski, T.; Pabian, W.; Gurgul, A.; Kapusta, P.; Ludwig-Słomczyńska, A.H.; Wołkow, P.P.; Brzychczy-Włoch, M. Next-Generation Sequencing as a Tool to Detect Vaginal Microbiota Disturbances during Pregnancy. Microorganisms 2020, 8, 1813. [Google Scholar] [CrossRef]
  13. Yao, T.; Wang, Z.; Liang, X.; Liu, C.; Yu, Z.; Han, X.; Liu, R.; Liu, Y.; Liu, C.; Chen, L. Signatures of vaginal microbiota by 16S rRNA gene: Potential bio-geographical application in Chinese Han from three regions of China. Int. J. Leg. Med. 2021, 135, 1213–1224. [Google Scholar] [CrossRef]
  14. Zhou, X.; Brown, C.J.; Abdo, Z.; Davis, C.C.; Hansmann, M.A.; Joyce, P.; Foster, J.A.; Forney, L.J. Differences in the composition of vaginal microbial communities found in healthy Caucasian and black women. ISME J. 2007, 1, 121–133. [Google Scholar] [CrossRef] [Green Version]
  15. Ravel, J.; Gajer, P.; Abdo, Z.; Schneider, G.M.; Koenig, S.S.; McCulle, S.L.; Karlebach, S.; Gorle, R.; Russell, J.; Tacket, C.O.; et al. Vaginal microbiome of reproductive-age women. Proc. Natl. Acad. Sci. USA 2011, 108 (Suppl. 1), 4680–4687. [Google Scholar] [CrossRef] [Green Version]
  16. DiGiulio, D.B.; Callahan, B.J.; McMurdie, P.J.; Costello, E.K.; Lyell, D.J.; Robaczewska, A.; Sun, C.L.; Goltsman, D.S.; Wong, R.J.; Shaw, G.; et al. Temporal and spatial variation of the human microbiota during pregnancy. Proc. Natl. Acad. Sci. USA 2015, 112, 11060–11065. [Google Scholar] [CrossRef] [Green Version]
  17. MacIntyre, D.A.; Chandiramani, M.; Lee, Y.S.; Kindinger, L.; Smith, A.; Angelopoulos, N.; Lehne, B.; Arulkumaran, S.; Brown, R.; Teoh, T.G.; et al. The vaginal microbiome during pregnancy and the postpartum period in a European population. Sci. Rep. 2015, 5, 8988. [Google Scholar] [CrossRef] [Green Version]
  18. Romero, R.; Hassan, S.S.; Gajer, P.; Tarca, A.L.; Fadrosh, D.W.; Nikita, L.; Galuppi, M.; Lamont, R.F.; Chaemsaithong, P.; Miranda, J.; et al. The composition and stability of the vaginal microbiota of normal pregnant women is different from that of non-pregnant women. Microbiome 2014, 2, 4. [Google Scholar] [CrossRef] [Green Version]
  19. Freitas, A.C.; Chaban, B.; Bocking, A.; Rocco, M.; Yang, S.; Hill, J.E.; Money, D.M. The vaginal microbiome of pregnant women is less rich and diverse, with lower prevalence of Mollicutes, compared to non-pregnant women. Sci. Rep. 2017, 7, 9212. [Google Scholar] [CrossRef] [Green Version]
  20. Siiteri, P.K.; MacDonald, P.C. Placental estrogen biosynthesis during human pregnancy. J. Clin. Endocrinol. Metab. 1966, 26, 751–761. [Google Scholar] [CrossRef]
  21. Fettweis, J.M.; Serrano, M.G.; Girerd, P.H.; Jefferson, K.K.; Buck, G.A. A new era of the vaginal microbiome: Advances using next-generation sequencing. Chem. Biodivers. 2012, 9, 965–976. [Google Scholar] [CrossRef] [PubMed]
  22. Amabebe, E.; Anumba, D.O.C. The Vaginal Microenvironment: The Physiologic Role of Lactobacilli. Front. Med. 2018, 5, 181. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Sakai, M.; Ishiyama, A.; Tabata, M.; Sasaki, Y.; Yoneda, S.; Shiozaki, A.; Saito, S. Relationship between cervical mucus interleukin-8 concentrations and vaginal bacteria in pregnancy. Am. J. Reprod. Immunol. 2004, 52, 106–112. [Google Scholar] [CrossRef] [PubMed]
  24. Witkin, S.S.; Linhares, I.M. Why do lactobacilli dominate the human vaginal microbiota? BJOG Int. J. Obstet. Gynaecol. 2017, 124, 606–611. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Hillier, S.L.; Nugent, R.P.; Eschenbach, D.A.; Krohn, M.A.; Gibbs, R.S.; Martin, D.H.; Cotch, M.F.; Edelman, R.; Pastorek, J.G., II; Rao, A.V.; et al. Association between bacterial vaginosis and preterm delivery of a low-birth-weight infant. The Vaginal Infections and Prematurity Study Group. New Engl. J. Med. 1995, 333, 1737–1742. [Google Scholar] [CrossRef] [PubMed]
  26. Flynn, C.A.; Helwig, A.L.; Meurer, L.N. Bacterial vaginosis in pregnancy and the risk of prematurity: A meta-analysis. J. Fam. Pract. 1999, 48, 885–892. [Google Scholar]
  27. Fettweis, J.M.; Brooks, J.P.; Serrano, M.G.; Sheth, N.U.; Girerd, P.H.; Edwards, D.J.; Strauss, J.F.; The Vaginal Microbiome, C.; Jefferson, K.K.; Buck, G.A. Differences in vaginal microbiome in African American women versus women of European ancestry. Microbiol. 2014, 160, 2272–2282. [Google Scholar] [CrossRef] [Green Version]
  28. Aagaard, K.; Riehle, K.; Ma, J.; Segata, N.; Mistretta, T.A.; Coarfa, C.; Raza, S.; Rosenbaum, S.; Van den Veyver, I.; Milosavljevic, A.; et al. A metagenomic approach to characterization of the vaginal microbiome signature in pregnancy. PLoS ONE 2012, 7, e36466. [Google Scholar] [CrossRef]
  29. Nasioudis, D.; Forney, L.J.; Schneider, G.M.; Gliniewicz, K.; France, M.; Boester, A.; Sawai, M.; Scholl, J.; Witkin, S.S. Influence of Pregnancy History on the Vaginal Microbiome of Pregnant Women in their First Trimester. Sci. Rep. 2017, 7, 10201. [Google Scholar] [CrossRef]
  30. Walther-António, M.R.; Jeraldo, P.; Berg Miller, M.E.; Yeoman, C.J.; Nelson, K.E.; Wilson, B.A.; White, B.A.; Chia, N.; Creedon, D.J. Pregnancy’s stronghold on the vaginal microbiome. PLoS ONE 2014, 9, e98514. [Google Scholar] [CrossRef] [Green Version]
  31. Forney, L.J.; Gajer, P.; Williams, C.J.; Schneider, G.M.; Koenig, S.S.; McCulle, S.L.; Karlebach, S.; Brotman, R.M.; Davis, C.C.; Ault, K.; et al. Comparison of self-collected and physician-collected vaginal swabs for microbiome analysis. J. Clin. Microbiol. 2010, 48, 1741–1748. [Google Scholar] [CrossRef]
  32. Schachter, J.; McCormack, W.M.; Chernesky, M.A.; Martin, D.H.; Van Der Pol, B.; Rice, P.A.; Hook, E.W., III; Stamm, W.E.; Quinn, T.C.; Chow, J.M. Vaginal swabs are appropriate specimens for diagnosis of genital tract infection with Chlamydia trachomatis. J. Clin. Microbiol. 2003, 41, 3784–3789. [Google Scholar] [CrossRef] [Green Version]
  33. Bolyen, E.; Rideout, J.R.; Dillon, M.R.; Bokulich, N.A.; Abnet, C.C.; Al-Ghalith, G.A.; Alexander, H.; Alm, E.J.; Arumugam, M.; Asnicar, F.; et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 2019, 37, 852–857. [Google Scholar] [CrossRef]
  34. Oliver, A.; LaMere, B.; Weihe, C.; Wandro, S.; Lindsay, K.L.; Wadhwa, P.D.; Mills, D.A.; Pride, D.T.; Fiehn, O.; Northen, T.; et al. Cervicovaginal Microbiome Composition Is Associated with Metabolic Profiles in Healthy Pregnancy. mBio 2020, 11. [Google Scholar] [CrossRef]
  35. Fettweis, J.M.; Serrano, M.G.; Brooks, J.P.; Edwards, D.J.; Girerd, P.H.; Parikh, H.I.; Huang, B.; Arodz, T.J.; Edupuganti, L.; Glascock, A.L.; et al. The vaginal microbiome and preterm birth. Nat. Med. 2019, 25, 1012–1021. [Google Scholar] [CrossRef] [Green Version]
  36. Romero, R.; Hassan, S.S.; Gajer, P.; Tarca, A.L.; Fadrosh, D.W.; Bieda, J.; Chaemsaithong, P.; Miranda, J.; Chaiworapongsa, T.; Ravel, J. The vaginal microbiota of pregnant women who subsequently have spontaneous preterm labor and delivery and those with a normal delivery at term. Microbiome 2014, 2, 18. [Google Scholar] [CrossRef] [Green Version]
  37. Hemalatha, R.; Ramalaxmi, B.A.; Swetha, E.; Balakrishna, N.; Mastromarino, P. Evaluation of vaginal pH for detection of bacterial vaginosis. Indian J. Med. Res. 2013, 138, 354–359. [Google Scholar]
  38. Mohamed, I.; Zakeer, S.; Azab, M.; Hanora, A. Changes in Vaginal Microbiome in Pregnant and Nonpregnant Women with Bacterial Vaginosis: Toward Microbiome Diagnostics? Omics: A J. Integr. Biol. 2020, 24, 602–614. [Google Scholar] [CrossRef]
  39. Mattei, V.; Murugesan, S.; Al Hashmi, M.; Mathew, R.; James, N.; Singh, P.; Kumar, M.; Lakshmanan, A.P.; Terranegra, A.; Al Khodor, S.; et al. Evaluation of Methods for the Extraction of Microbial DNA From Vaginal Swabs Used for Microbiome Studies. Front. Cell. Infect. Microbiol. 2019, 9, 197. [Google Scholar] [CrossRef]
  40. Illumina. 16S Metagenomic Sequencing Library Preparation Protocol: Preparing 16S Ribosomal RNA Gene Amplicons for the Illumina MiSeq System. Part no. 15044223 Rev B. Illumina, San Diego, CA, USA. Available online: https://www.illumina.com/content/dam/illumina-support/documents/documentation/chemistry_documentation/16s/16s-metagenomic-library-prep-guide-15044223-b.pdf (accessed on 12 June 2020).
  41. Callahan, B.J.; McMurdie, P.J.; Rosen, M.J.; Han, A.W.; Johnson, A.J.; Holmes, S.P. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 2016, 13, 581–583. [Google Scholar] [CrossRef] [Green Version]
  42. Mohsen, A.; Park, J.; Chen, Y.A.; Kawashima, H.; Mizuguchi, K. Impact of quality trimming on the efficiency of reads joining and diversity analysis of Illumina paired-end reads in the context of QIIME1 and QIIME2 microbiome analysis frameworks. BMC Bioinform. 2019, 20, 581. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. DeSantis, T.Z.; Hugenholtz, P.; Larsen, N.; Rojas, M.; Brodie, E.L.; Keller, K.; Huber, T.; Dalevi, D.; Hu, P.; Andersen, G.L. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 2006, 72, 5069–5072. [Google Scholar] [CrossRef] [PubMed]
  44. Chong, J.; Liu, P.; Zhou, G.; Xia, J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat. Protoc. 2020, 15, 799–821. [Google Scholar] [CrossRef] [PubMed]
  45. Dhariwal, A.; Chong, J.; Habib, S.; King, I.L.; Agellon, L.B.; Xia, J. MicrobiomeAnalyst: A web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 2017, 45, W180–W188. [Google Scholar] [CrossRef] [PubMed]
  46. France, M.T.; Ma, B.; Gajer, P.; Brown, S.; Humphrys, M.S.; Holm, J.B.; Waetjen, L.E.; Brotman, R.M.; Ravel, J. VALENCIA: A nearest centroid classification method for vaginal microbial communities based on composition. Microbiome 2020, 8, 166. [Google Scholar] [CrossRef]
  47. A framework for human microbiome research. Nature 2012, 486, 215–221. [CrossRef] [Green Version]
  48. O’Hanlon, D.E.; Moench, T.R.; Cone, R.A. Vaginal pH and microbicidal lactic acid when lactobacilli dominate the microbiota. PLoS ONE 2013, 8, e80074. [Google Scholar] [CrossRef]
  49. Borges, S.; Silva, J.; Teixeira, P. The role of lactobacilli and probiotics in maintaining vaginal health. Arch. Gynecol. Obstet. 2014, 289, 479–489. [Google Scholar] [CrossRef]
  50. Pino, A.; Bartolo, E.; Caggia, C.; Cianci, A.; Randazzo, C.L. Detection of vaginal lactobacilli as probiotic candidates. Sci. Rep. 2019, 9, 3355. [Google Scholar] [CrossRef] [Green Version]
  51. Gautam, R.; Borgdorff, H.; Jespers, V.; Francis, S.C.; Verhelst, R.; Mwaura, M.; Delany-Moretlwe, S.; Ndayisaba, G.; Kyongo, J.K.; Hardy, L.; et al. Correlates of the molecular vaginal microbiota composition of African women. BMC Infect. Dis. 2015, 15, 86. [Google Scholar] [CrossRef] [Green Version]
  52. Jespers, V.; van de Wijgert, J.; Cools, P.; Verhelst, R.; Verstraelen, H.; Delany-Moretlwe, S.; Mwaura, M.; Ndayisaba, G.F.; Mandaliya, K.; Menten, J.; et al. The significance of Lactobacillus crispatus and L. vaginalis for vaginal health and the negative effect of recent sex: A cross-sectional descriptive study across groups of African women. BMC Infect. Dis. 2015, 15, 115. [Google Scholar] [CrossRef] [Green Version]
  53. Holm, J.B.; France, M.T.; Ma, B.; McComb, E.; Robinson, C.K.; Mehta, A.; Tallon, L.J.; Brotman, R.M.; Ravel, J. Comparative Metagenome-Assembled Genome Analysis of “Candidatus Lachnocurva vaginae”, Formerly Known as Bacterial Vaginosis-Associated Bacterium-1 (BVAB1). Front. Cell. Infect. Microbiol. 2020, 10, 117. [Google Scholar] [CrossRef]
  54. Callahan, B.J.; DiGiulio, D.B.; Goltsman, D.S.A.; Sun, C.L.; Costello, E.K.; Jeganathan, P.; Biggio, J.R.; Wong, R.J.; Druzin, M.L.; Shaw, G.M.; et al. Replication and refinement of a vaginal microbial signature of preterm birth in two racially distinct cohorts of US women. Proc. Natl. Acad. Sci. USA 2017, 114, 9966–9971. [Google Scholar] [CrossRef] [Green Version]
  55. Park, S.; You, Y.A.; Kim, Y.H.; Kwon, E.; Ansari, A.; Kim, S.M.; Lee, G.; Hur, Y.M.; Jung, Y.J.; Kim, K.; et al. Ureaplasma and Prevotella colonization with Lactobacillus abundance during pregnancy facilitates term birth. Sci. Rep. 2022, 12, 10148. [Google Scholar] [CrossRef]
  56. Gilbert, N.M.; Foster, L.R.; Cao, B.; Yin, Y.; Mysorekar, I.U.; Lewis, A.L. Gardnerella vaginalis promotes group B Streptococcus vaginal colonization, enabling ascending uteroplacental infection in pregnant mice. Am. J. Obstet. Gynecol. 2021, 224, 530.e531. [Google Scholar] [CrossRef]
  57. Ling, Z.; Liu, X.; Luo, Y.; Wu, X.; Yuan, L.; Tong, X.; Li, L.; Xiang, C. Associations between vaginal pathogenic community and bacterial vaginosis in Chinese reproductive-age women. PLoS ONE 2013, 8, e76589. [Google Scholar] [CrossRef]
  58. Muzny, C.A.; Łaniewski, P.; Schwebke, J.R.; Herbst-Kralovetz, M.M. Host-vaginal microbiota interactions in the pathogenesis of bacterial vaginosis. Curr. Opin. Infect. Dis. 2020, 33, 59–65. [Google Scholar] [CrossRef]
  59. Muzny, C.A.; Taylor, C.M.; Swords, W.E.; Tamhane, A.; Chattopadhyay, D.; Cerca, N.; Schwebke, J.R. An Updated Conceptual Model on the Pathogenesis of Bacterial Vaginosis. J. Infect. Dis. 2019, 220, 1399–1405. [Google Scholar] [CrossRef]
  60. Teixeira, G.S.; Soares-Brandão, K.L.K.; Branco, K.; Sampaio, J.L.M.; Nardi, R.M.D.; Mendonça, M.; Almeida, R.B.; Farias, L.M.; Carvalho, M.A.R.; Nicoli, J.R. Antagonism and synergism in Gardnerella vaginalis strains isolated from women with bacterial vaginosis. J. Med. Microbiol. 2010, 59, 891–897. [Google Scholar] [CrossRef]
  61. Torcia, M.G. Interplay among Vaginal Microbiome, Immune Response and Sexually Transmitted Viral Infections. Int. J. Mol. Sci. 2019, 20, 266. [Google Scholar] [CrossRef] [Green Version]
  62. Graspeuntner, S.; Loeper, N.; Künzel, S.; Baines, J.F.; Rupp, J. Selection of validated hypervariable regions is crucial in 16S-based microbiota studies of the female genital tract. Sci. Rep. 2018, 8, 9678. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Prince, A.L.; Chu, D.M.; Seferovic, M.D.; Antony, K.M.; Ma, J.; Aagaard, K.M. The perinatal microbiome and pregnancy: Moving beyond the vaginal microbiome. Cold Spring Harb. Perspect. Med. 2015, 5, a023051. [Google Scholar] [CrossRef] [PubMed]
  64. Estaki, M.; Jiang, L.; Bokulich, N.A.; McDonald, D.; González, A.; Kosciolek, T.; Martino, C.; Zhu, Q.; Birmingham, A.; Vázquez-Baeza, Y.; et al. QIIME 2 Enables Comprehensive End-to-End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data. Curr. Protoc. Bioinform. 2020, 70, e100. [Google Scholar] [CrossRef] [PubMed]
  65. Chen, H.M.; Chang, T.H.; Lin, F.M.; Liang, C.; Chiu, C.M.; Yang, T.L.; Yang, T.; Huang, C.Y.; Cheng, Y.N.; Chang, Y.A.; et al. Vaginal microbiome variances in sample groups categorized by clinical criteria of bacterial vaginosis. BMC Genom. 2018, 19, 876. [Google Scholar] [CrossRef] [PubMed]
  66. Ceccarani, C.; Foschi, C.; Parolin, C.; D’Antuono, A.; Gaspari, V.; Consolandi, C.; Laghi, L.; Camboni, T.; Vitali, B.; Severgnini, M.; et al. Diversity of vaginal microbiome and metabolome during genital infections. Sci. Rep. 2019, 9, 14095. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Lee, Y.H.; Kang, G.U.; Jeon, S.Y.; Tagele, S.B.; Pham, H.Q.; Kim, M.S.; Ahmad, S.; Jung, D.R.; Park, Y.J.; Han, H.S.; et al. Vaginal Microbiome-Based Bacterial Signatures for Predicting the Severity of Cervical Intraepithelial Neoplasia. Diagnostics 2020, 10, 1013. [Google Scholar] [CrossRef] [PubMed]
  68. Kumar, S.; Kumari, N.; Talukdar, D.; Kothidar, A.; Sarkar, M.; Mehta, O.; Kshetrapal, P.; Wadhwa, N.; Thiruvengadam, R.; Desiraju, B.K.; et al. The Vaginal Microbial Signatures of Preterm Birth Delivery in Indian Women. Front. Cell. Infect. Microbiol. 2021, 11, 622474. [Google Scholar] [CrossRef]
  69. Wang, J.; Li, Z.; Ma, X.; Du, L.; Jia, Z.; Cui, X.; Yu, L.; Yang, J.; Xiao, L.; Zhang, B.; et al. Translocation of vaginal microbiota is involved in impairment and protection of uterine health. Nat. Commun. 2021, 12, 4191. [Google Scholar] [CrossRef]
  70. Hyman, R.W.; Fukushima, M.; Jiang, H.; Fung, E.; Rand, L.; Johnson, B.; Vo, K.C.; Caughey, A.B.; Hilton, J.F.; Davis, R.W.; et al. Diversity of the vaginal microbiome correlates with preterm birth. Reprod. Sci. 2014, 21, 32–40. [Google Scholar] [CrossRef] [Green Version]
  71. Ranjan, R.; Rani, A.; Metwally, A.; McGee, H.S.; Perkins, D.L. Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. Biochem. Biophys. Res. Commun. 2016, 469, 967–977. [Google Scholar] [CrossRef] [Green Version]
  72. Peterson, D.; Bonham, K.S.; Rowland, S.; Pattanayak, C.W.; Klepac-Ceraj, V. Comparative Analysis of 16S rRNA Gene and Metagenome Sequencing in Pediatric Gut Microbiomes. Front. Microbiol. 2021, 12, 670336. [Google Scholar] [CrossRef]
  73. Feehily, C.; Crosby, D.; Walsh, C.J.; Lawton, E.M.; Higgins, S.; McAuliffe, F.M.; Cotter, P.D. Shotgun sequencing of the vaginal microbiome reveals both a species and functional potential signature of preterm birth. NPJ Biofilms Microbiomes 2020, 6, 50. [Google Scholar] [CrossRef]
  74. O’Callaghan, J.L.; Willner, D.; Buttini, M.; Huygens, F.; Pelzer, E.S. Limitations of 16S rRNA Gene Sequencing to Characterize Lactobacillus Species in the Upper Genital Tract. Front. Cell Dev. Biol. 2021, 9, 641921. [Google Scholar] [CrossRef]
  75. Giampaoli, S.; DeVittori, E.; Valeriani, F.; Berti, A.; Romano Spica, V. Informativeness of NGS Analysis for Vaginal Fluid Identification. J. Forensic Sci. 2017, 62, 192–196. [Google Scholar] [CrossRef]
  76. Ladetto, M.; Brüggemann, M.; Monitillo, L.; Ferrero, S.; Pepin, F.; Drandi, D.; Barbero, D.; Palumbo, A.; Passera, R.; Boccadoro, M.; et al. Next-generation sequencing and real-time quantitative PCR for minimal residual disease detection in B-cell disorders. Leukemia 2014, 28, 1299–1307. [Google Scholar] [CrossRef]
Microorganisms 11 00139 g001 550
Figure 1. Relative abundance of taxa across the three trimesters of pregnancy in comparison to non-pregnant state. (A) Relative abundance of taxa at phylum level. (B) Relative abundance of taxa at genus level. (C) Relative abundance of Lactobacillus species. (D) Relative abundance of non-Lactobacillus species (Figure 1D is re-represented as Supplementary Figure S4 with different y-scale to show the relative abundance of minor non-Lactobacillus species). Negative control represents samples with empty swab for DNA extraction and amplified nuclease-free water without extracted DNA.
Figure 1. Relative abundance of taxa across the three trimesters of pregnancy in comparison to non-pregnant state. (A) Relative abundance of taxa at phylum level. (B) Relative abundance of taxa at genus level. (C) Relative abundance of Lactobacillus species. (D) Relative abundance of non-Lactobacillus species (Figure 1D is re-represented as Supplementary Figure S4 with different y-scale to show the relative abundance of minor non-Lactobacillus species). Negative control represents samples with empty swab for DNA extraction and amplified nuclease-free water without extracted DNA.
Microorganisms 11 00139 g001
Microorganisms 11 00139 g002 550
Figure 2. Community State Assignment (CST) of vaginal microbiomes across the three trimesters of pregnancy in comparison to non-pregnant state using VALENCIA. (A) Similarity of vaginal samples to the assigned CST and subCST designations. (B) CST classification of the vaginal samples, as revealed by VALENCIA.
Figure 2. Community State Assignment (CST) of vaginal microbiomes across the three trimesters of pregnancy in comparison to non-pregnant state using VALENCIA. (A) Similarity of vaginal samples to the assigned CST and subCST designations. (B) CST classification of the vaginal samples, as revealed by VALENCIA.
Microorganisms 11 00139 g002
Microorganisms 11 00139 g003 550
Figure 3. Alpha diversity indices of vaginal microbial communities across the three trimesters of pregnancy in comparison to non-pregnant state. Alpha diversity, as calculated by the (A) Observed (B) Chao1 (C) Shannon (D) Simpson Alpha diversity indices. A p value < 0.05 is considered statistically significant, as calculated by pairwise Kruskal–Wallis test. Dunn pairwise test was performed.
Figure 3. Alpha diversity indices of vaginal microbial communities across the three trimesters of pregnancy in comparison to non-pregnant state. Alpha diversity, as calculated by the (A) Observed (B) Chao1 (C) Shannon (D) Simpson Alpha diversity indices. A p value < 0.05 is considered statistically significant, as calculated by pairwise Kruskal–Wallis test. Dunn pairwise test was performed.
Microorganisms 11 00139 g003
Microorganisms 11 00139 g004 550
Figure 4. Pielou’s evenness Alpha diversity of vaginal microbial communities across the three trimesters of pregnancy in comparison to non-pregnant state. Holm-adjusted p values were used to indicate significance. Dunn pairwise test was performed.
Figure 4. Pielou’s evenness Alpha diversity of vaginal microbial communities across the three trimesters of pregnancy in comparison to non-pregnant state. Holm-adjusted p values were used to indicate significance. Dunn pairwise test was performed.
Microorganisms 11 00139 g004
Microorganisms 11 00139 g005 550
Figure 5. Beta diversity indices shown as two-dimensional principal coordinate analysis (PCoA) plots of vaginal microbial communities across the three trimesters of pregnancy in comparison to non-pregnant state. Beta diversity was calculated by (A) Bray–Curtis index and (B) Jaccard index (C) unweighted UniFrac test (D) weighted UniFrac test. A p value < 0.05 is considered statistically significant.
Figure 5. Beta diversity indices shown as two-dimensional principal coordinate analysis (PCoA) plots of vaginal microbial communities across the three trimesters of pregnancy in comparison to non-pregnant state. Beta diversity was calculated by (A) Bray–Curtis index and (B) Jaccard index (C) unweighted UniFrac test (D) weighted UniFrac test. A p value < 0.05 is considered statistically significant.
Microorganisms 11 00139 g005
Microorganisms 11 00139 g006 550
Figure 6. Correlation plot for dominant taxa during pregnancy. (A) Correlation plot for dominant phyla. (B) Correlation plot for dominant genera. (C) Correlation plot for dominant species. Positive correlation strength relationship is shown in red and negative correlation strength relationship is shown in blue. The plot was generated using Spearman’s correlation coefficients between each taxon and all other taxa.
Figure 6. Correlation plot for dominant taxa during pregnancy. (A) Correlation plot for dominant phyla. (B) Correlation plot for dominant genera. (C) Correlation plot for dominant species. Positive correlation strength relationship is shown in red and negative correlation strength relationship is shown in blue. The plot was generated using Spearman’s correlation coefficients between each taxon and all other taxa.
Microorganisms 11 00139 g006
Table
Table 1. Distribution of samples in each community state-type according to pregnancy status.
Table 1. Distribution of samples in each community state-type according to pregnancy status.
CST/Pregnancy StatusIIIIIIV-BIV-CVTotal
III-A III-BIV-C0IV-C1IV-C3
Non-pregnant control group1 (8.33%)4 (33.33%)5 (41.67%)---1 (8.33%)1 (8.33%)12
Trimester—first1 (8.33%)4 (33.33%)2 (16.67%)1 (8.33%)1 (8.33%)-1 (8.33%)2 (16.67%)12
Trimester—second-5 (41.67%)6 (50%)----1 (8.33%)12
Trimester—third2 (16.67%)2 (16.67%)4 (33.33%)--1 (8.33%)-3 (25%)12
Total4 (8.33%)15 (31.25%)17 (35.42%)1 (2.08%)1 (2.08%)1 (2.08%)2 (4.17%)7 (14.58%)48
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Amin, M.E.; Azab, M.; Hanora, A.; Atwa, K.; Shabayek, S. Compositional Changes in the Vaginal Bacterial Microbiome of Healthy Pregnant Women across the Three Gestational Trimesters in Ismailia, Egypt. Microorganisms 2023, 11, 139. https://doi.org/10.3390/microorganisms11010139

AMA Style

Amin ME, Azab M, Hanora A, Atwa K, Shabayek S. Compositional Changes in the Vaginal Bacterial Microbiome of Healthy Pregnant Women across the Three Gestational Trimesters in Ismailia, Egypt. Microorganisms. 2023; 11(1):139. https://doi.org/10.3390/microorganisms11010139

Chicago/Turabian Style

Amin, Mariam E., Marwa Azab, Amro Hanora, Khaled Atwa, and Sarah Shabayek. 2023. "Compositional Changes in the Vaginal Bacterial Microbiome of Healthy Pregnant Women across the Three Gestational Trimesters in Ismailia, Egypt" Microorganisms 11, no. 1: 139. https://doi.org/10.3390/microorganisms11010139

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop